In a cross-sectional study of 20 Massachusetts long-term care (LTC) facilities, 19% (n = 449) of residents received antibiotics, with approximately one-third having a documented penicillin allergy. This documented allergy decreased the likelihood of prescribing beta-lactam antibiotics for infections. Improved penicillin allergy assessments and delabeling could optimize antibiotic prescribing in LTC settings.

Adipocytokines, which are secreted during fetal development by both mothers and fetuses, may influence fetal lung development, but little human data are available. We used data from the HOME Study to investigate the associations of cord blood adipocytokine concentrations with children’s lung forced expiratory volume (FEV1; N = 160) and their risk of wheeze (N = 281). We measured umbilical cord serum adipocytokine concentrations using enzyme-linked immunosorbent assays and FEV1 using a portable spirometer at ages 4 and 5 to calculate the percent predicted FEV1 (%FEV1). Parents completed standardized questionnaires of their child’s wheeze symptoms every 6 months from birth to age 5, then again at ages 6 and 8. We used multivariable linear mixed models and modified Poisson regression with generalized estimating equations to estimate associations of adipocytokine concentrations (log2-transformed) with children’s %FEV1 and the risk of wheeze, respectively, adjusting for sociodemographic, perinatal, and child factors. Cord serum leptin was not associated with children’s %FEV1. Higher cord serum adiponectin concentrations were associated with higher %FEV1 in girls (β = 3.1, 95% confidence interval [CI]: 0.6, 5.6), but not in boys (β = −1.3, 95% CI: −5.9, 3.3) (sex × adiponectin p-value = 0.05). Higher leptin was associated with lower risk of wheeze in girls (RR = 0.74, 95% CI: 0.66, 0.84), but not boys (RR = 0.87, 95% CI: 0.69, 1.11) (sex × leptin p-value = 0.01). In contrast, higher adiponectin concentrations were associated with lower risk of wheeze (RR = 0.84, 95% CI: 0.73, 0.96) in both boys and girls. These data suggest that fetal adipocytokines may impact lung development and function in early childhood. Future studies are needed to confirm these findings and explore the mechanisms underlying these associations.

Background: Infection prevention and control (IPC) workflows are often retrospective and manual. New tools, however, have entered the field to facilitate rapid prospective monitoring of infections in hospitals. Although artificial intelligence (AI)–enabled platforms facilitate timely, on-demand integration of clinical data feeds with pathogen whole-genome sequencing (WGS), a standardized workflow to fully harness the power of such tools is lacking. We report a novel, evidence-based workflow that promotes quicker infection surveillance via AI-assisted clinical and WGS data analysis. The algorithm suggests clusters based on a combination of similar minimum inhibitory concentration (MIC) data, timing of sample collection, and shared location stays between patients. It helps to proactively guide IPC professionals during investigation of infectious outbreaks and surveillance of multidrug-resistant organisms and healthcare-acquired infections. Methods: Our team established a 1-year workgroup comprised of IPC practitioners, clinical experts, and scientists in the field. We held weekly roundtables to study lessons learned in an ongoing surveillance effort at a tertiary care hospital—utilizing Philips IntelliSpace Epidemiology (ISEpi), an AI-powered system—to understand how such a tool can enhance practice. Based on real-time case discussions and evidence from the literature, a workflow guidance tool and checklist were codified. Results: In our workflow, data-informed clusters posed by ISEpi underwent triage and expert follow-up analysis to assess: (1) likelihood of transmission(s); (2) potential vector(s) identity; (3) need to request WGS; and (4) intervention(s) to be pursued, if warranted. In a representative sample (spanning October 17, 2019, to November 7, 2019) of 67 total isolates suggested for inclusion in 19 unique cluster investigations, we determined that 9 investigations merited follow-up. Collectively, these 9 investigations involved 21 patients and required 115 minutes to review in ISEpi and an additional 70 minutes of review outside of ISEpi. After review, 6 investigations were deemed unlikely to represent a transmission; the other 3 had potential to represent transmission for which interventions would be performed. Conclusions: This study offers an important framework for adaptation of existing infection control workflow strategies to leverage the utility of rapidly integrated clinical and WGS data. This workflow can also facilitate time-sensitive decisions regarding sequencing of specific pathogens given the preponderance of available clinical data supporting investigations. In this regard, our work sets a new standard of practice: precision infection prevention (PIP). Ongoing effort is aimed at development of AI-powered capabilities for enterprise-level quality and safety improvement initiatives.

Funding: Philips Healthcare provided support for this study.

Disclosures: Alan Doty and Juan Jose Carmona report salary from Philips Healthcare.

We applied three statistical classification techniques—linear discriminant analysis (LDA), logistic regression, and random forests—to three astronomical datasets associated with searches for interstellar masers. We compared the performance of these methods in identifying whether specific mid-infrared or millimetre continuum sources are likely to have associated interstellar masers. We also discuss the interpretability of the results of each classification technique. Non-parametric methods have the potential to make accurate predictions when there are complex relationships between critical parameters. We found that for the small datasets the parametric methods logistic regression and LDA performed best, for the largest dataset the non-parametric method of random forests performed with comparable accuracy to parametric techniques, rather than any significant improvement. This suggests that at least for the specific examples investigated here accuracy of the predictions obtained is not being limited by the use of parametric models. We also found that for LDA, transformation of the data to match a normal distribution led to a significant improvement in accuracy. The different classification techniques had significant overlap in their predictions; further astronomical observations will enable the accuracy of these predictions to be tested.

Although Lievens and Motowidlo (2016) made a strong case for reconceptualizing situational judgment tests (SJTs) as measures of general domain knowledge, we disagree with their view that the judgment or assessment of the situation itself is not important. We contend that situation assessment is an integral yet ignored factor in SJTs and that both general domain knowledge and situation assessment are needed to better understand how SJTs work.

We report a severe outbreak of Clostridium difficile infection. According to a chart review, half of the patients who received treatment for bacterial pneumonia before they developed C. difficile infection may not have had pneumonia. Excessive use of the hospital's new pneumonia care plan during the influenza season may have contributed to the intensity of this outbreak.